Update reference-managed-online-endpoints-vm-sku-list.md

Author: dem108

articles/machine-learning/reference-managed-online-endpoints-vm-sku-list.md

@@ -30,4 +30,4 @@ This table shows the VM SKUs that are supported for Azure Machine Learning manag
 | X-Large| - | Standard_F32s_v2 <br/> Standard_F48s_v2 <br/> Standard_F64s_v2 <br/> Standard_F72s_v2 <br/> Standard_FX24mds <br/> Standard_FX36mds <br/> Standard_FX48mds| Standard_E32s_v3 <br/> Standard_E48s_v3 <br/> Standard_E64s_v3 | Standard_ND40rs_v2 <br/> Standard_ND96asr_v4 <br/> Standard_ND96amsr_A100_v4 <br/>|
 
 > [!CAUTION]
-> `Standard_DS1_v2` and `Standard_F2s_v2` may be too small for bigger models and may lead to container termination due to insufficient memory, not enough space on the disk, or probe failure as it takes too long to initiate the container. If you want to reduce the cost of deploying multiple models with managed online endpoint, see [the example for multi models](how-to-deploy-online-endpoints.md#use-more-than-one-model). If you face [OutOfQuota errors](how-to-troubleshoot-online-endpoints?tabs=cli#error-outofquota) or [ReourceNotReady errors](how-to-troubleshoot-online-endpoints?tabs=cli#error-resourcenotready), try bigger VM SKUs.
+> `Standard_DS1_v2` and `Standard_F2s_v2` may be too small for bigger models and may lead to container termination due to insufficient memory, not enough space on the disk, or probe failure as it takes too long to initiate the container. If you want to reduce the cost of deploying multiple models with managed online endpoint, see [the example for multi models](how-to-deploy-online-endpoints.md#use-more-than-one-model). If you face [OutOfQuota errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-outofquota) or [ReourceNotReady errors](how-to-troubleshoot-online-endpoints.md?tabs=cli#error-resourcenotready), try bigger VM SKUs.